Civic technology is a fast-developing segment that holds huge potential for a new generation of startups. A recent survey report on civic technology noted that the sector saw $430 million in investment in just the last two years. It’s not just a new market ripe with opportunity it’s crucial to our democracy. Crowdsourcing has proven to be an effective supplementary mechanism for public engagement in city government in order to use mutual knowledge in online communities to address such issues as a means of engaging people in urban design. Government needs new alternatives – alternatives of modern, superior tools and services that are offered at reasonable rates. An effective and easy-to-use civic technology platform enables wide participation. Response to, and a ‘conversation’ with, the users is very crucial for engagement, as is a feeling of being part of a society. These findings can contribute to the future design of civic technology platforms. In this research, we are trying to introduce a crowdsourcing platform, which will be helpful to people who are facing problems in their everyday practice because of the government services. This platform will gather the information from the trending twitter tweets for last month or so and try to identify which challenges public is confronting. Twitter for crowdsourcing as it is a simple social platform for questions and for the people who see the tweet to get an instant answer. These problems will be analyzed based on their significance which then will be made open to public for its solutions. The findings demonstrate how crowdsourcing tends to boost community engagement, enhances citizens ‘ views of their town and thus tends us find ways to enhance the city’s competitiveness, which faces some serious problems. Using of topic modeling with Latent Dirichlet Allocation (LDA) algorithm helped get categorized civic technology topics which was then validated by simple classification algorithm. While working on this research, we encountered some issues regarding to the tools that were available which we have discussed in the ‘Counter arguments’ section.

This paper talks about single image super resolution with deep learning based solution.

In this paper, an attempt has been made to understand the importance of a neural network-based chatbot system for movie-related queries.

This paper implements the automated classification of patient discharge notes into standard disease labels which includes the name of the diagnostic procedure required. In this approach, we use Convolutional Neural Networks to classify and represent complex features from the medical discharge summaries using the MT sample dataset. We make use of GloVE to have a pretrained model learn from it.

Multiple-input multiple-output (MIMO) is a key enabler for high data rates in mobile communications. However, it is challenging to design MIMO terminal antennas for LTE bands below 1 GHz, due to the conventional chassis offering only one resonant characteristic mode (CM). Recently, it was shown that minor structural changes can yield up to two additional resonant modes for designing two-port MIMO antennas. Nonetheless, the resulting bandwidth for the second port is relatively small. To simultaneously meet bandwidth and other practical requirements (including low profile and no off-ground clearance), a step-by-step approach for structural changes and feed design is applied in this work to exemplify the use of physical insights from CM analysis to achieve a competitive wideband two-port solution. The main novelty is that an entirely new mode is identified and appropriately tuned by structural modification for creating an additional resonance below 1 GHz. Moreover, two simple probe-feed ports are designed to jointly excite different subsets of four modes over frequency. In addition, far-field pattern orthogonality is guaranteed by the different phase shifts of the characteristic electric fields at the port locations. Furthermore, bulkier self-resonant antenna elements are avoided. To show design flexibility, a three-port version is also demonstrated.

Social media is a platform in which tons and tons of text are generated each and every day. The data is so large that cannot be easily understood, so this has paved a path to a new field in the information technology which is natural language processing. In this paper, the text data which is used for the classification is tweets that determines the state of the person according of the sentiments which is positive, negative and neutral. Emotions are the way of expression of the person’s feelings which has a high influence on the decision making tasks. Here we have proposed the text representation, Term Frequency Inverse Document Frequency (tfidf), Keras embedding along with the machine learning and deep learning algorithms for the purpose of the classification of the sentiments, out of which Logistics Regression machine learning based methods out performs well when the features is taken in the limited amount as the features increases Support Vector Machine (SVM) which is also one of the machine learning algorithm out performs well making a benchmark accuracy for this dataset as the 75.8%. For the research purpose the dataset has been made publically available.

During the last two decades, profound technological changes have taken place around us, supported by disruptive advances, both on software and hardware sides. An amalgamation of information, communication and AI is taking place, as well as the cross-fertilization of a wide range of concepts, referred to as the digital transformation. As a result of convergence phenomenon, the boundaries between different disciplines are eroding, necessitating a thorough discussion on what the engineering education should be like in the future. In this paper, after presenting a brief history of engineering education, the recent paradigm changes are discussed, which essentially stress that skills must prevail over degrees to deal with challenges posed by the megatrends of the fourth industrial revolution.

Communication for control-centric industrial applications is characterized by the requirements of very high reliability, very low and deterministic latency and high scalability. This paper proposes a novel solution for providing reliable and deterministic communication, through Wi-Fi, in industrial environments. The proposed solution, termed as HAR2D-Fi, adopts hybrid channel access mechanisms for achieving deterministic communication. It also provides temporal redundancy for enhanced reliability. HAR2D-Fi implements different medium access control (MAC) designs that build on the standard physical (PHY) layer. Such designs can be classified into two categories: (a) MAC designs with pre-defined (physical) time-slotted schedule, and (b) MAC designs with virtual time-slotted schedule. Performance evaluation, based on analysis and system-level simulations, demonstrates the viability of HAR2D-Fi for control-centric industrial applications.

In this age of digital advancements, where technologies are changing in a fraction of time. From the abacus which made tutoring math easy millennia back, to word processor which changed the way research paper is being written and presented. After every era, with the advancements in the technology has not only given shaped the education but also transformed it. There was a time when the education world of black on white changed to projected presentations. However, now in this paper, we believe to go beyond the two-dimensional space and create a whole new educational world for children. Augmented Reality (AR) has successfully made classroom learning more interactive and engaging for students as well as for teachers to deliver their lectures. AR is the combination of the real-world with computer- generated world. It is one of the most emerging fields in computer science. The conventional approach for learning can be stressful and to a certain extent less effective for some students. So, we propose a system in which students use smart devices like tablets, mobile, etc. that act as an alternative to boring supportive textbooks. Also, we plan to develop an application consisting of two modules like interactive learning and fun examination, a hybrid of the traditional approach and innovative practical illustrations of complicated concepts leading education in another dimension.

This is a research project which uses computer vision techniques to find out the empty parking spaces in any given Parking lot. It uses existing functions from OpenCV library in Python programming language to extract the parking lines from an image.

This research is based on developing a drones which have significant importance in the current market with many new inventions. In this paper; we tried to build a drone that is controlled by using a virtual assistant called Amazon Alexa. This drone will be using only the receiver other than the transmitter and also built-in services of amazon help us to create voice recognition understandable by the system. We have succeeded 70% of the work and achieved good results in connecting Amazon Alexa and Arduino Yun based drone.

Versatile configurable defected ground structures (CDGSs) for enhancing the performance of low profile antennas are introduced. It is shown that CDGS can significantly reduce mutual coupling (MC) between multiple antennas and suppress cross-polarization (XP) and enhance circular polarization (CP) excitation in single port low profile antennas for example. The key idea of CDGS is to construct defected ground structures (DGSs) from a grid of slots, which can be either opened or shorted with hardwires, so that they can be configured and optimized to enhance desired antenna performance characteristics. The importance and versatility of the CDGS approach is that it overcomes the issue of having to design bespoke DGS for each individual antenna design. Three design examples are provided to demonstrate the versatility of CDGSs for MC reduction, XP suppression and CP excitation. Experimental results demonstrate that MC can be reduced by up to 43 dB, XP can be suppressed by 15 dB and CP can be excited with 78 MHz (2.2%) 3-dB axial ratio (AR) bandwidth. The compactness and ease of fabrication also make the CDGS well suited to compact low profile internet of things (IoT) and wireless communication applications.

We introduce a rule base fuzzy technique on decomposed wavelet coefficients, to improve the wavelet edge representation. Our algorithm mitigates ‘incorrect’ responses, due primarily to the symmetries of directional derivative filters. Since the Discrete Wavelet Transform (DWT) coefficients are revealed from two dimensional symmetric filter banks and undermine some gradient information. These wavelet coefficients are prearranged into ‘if-then’ rule structure of a fuzzy inference system, to improve the wavelet edge representation.

Emerging of Cognitive Radio (CR) technology has provided optimistic solution for the dearth of spectrum by improving the spectrum utilization. The opportunistic use of the spectrum is enabled by spectrum sensing which is one of the key functionality of CR systems. To perform the interference free transmission in a cognitive radio networks, an important part for unlicensed user is to identify a licensed user with the help of spectrum sensing. Recently, the Cooperative Spectrum Sensing has been widely used in the literature where various scattered unlicensed users collaborate with each other to make the final sensing decision. This overcome the hidden terminal problem and also improve the overall reliability of the decisions made about the presence or absence of a licensed user. Each unlicensed user send the sensing results to the base station for final decision. However there exist some nodes which do not provide the correct sensing results to maximize their own profit which can highly degrade the CR network functionality. In this paper, a trust aware model is proposed for detection of misbehaving nodes such that their sensing reports can be filter out from the final result. The performance evaluation of the proposed scheme is done by checking its robustness and efficiency against various possible attacks.

We propose BSC, a Bitcoin Smart Contract implementation. It integrates the functionality of smart contracts into the Bitcoin system, giving developers the ability to build decentralized applications on Bitcoin. BSC will require a new hard fork, on which Bitcoin holders can use their existing funds directly. BSC combines the unlimited creative space of smart contracts and the vast network effect of Bitcoin, which will bring even more possibilities to the cryptocurrency world.

Arranging some magnets can intensify its magnetic field, as known as Halbach array. Magnets can be considered as a 3 dimensional array of magnetic dipoles. Optimizing these magnetic dipole directions, magnets can be more stronger. This article shows how to ditermine these directions, and how to magnetize ferromagnetic material with that directions.

According to kids’ learning process, an auto-encoder which can be split into two parts is designed. The two parts can work well separately. The top half is an abstract network which is trained by supervised learning and can be used to classify and regress. The bottom half is a concrete network which is accomplished by inverse function and trained by self-supervised learning. It can generate the input of abstract network from concept or label. The network can achieve its intended functionality through testing by mnist dataset and convolution neural network. Round function is added between the abstract network and concrete network in order to get the representative generation of class. The generation ability can be increased by adding jump connection and negative feedback. At last, the characteristics of the network is discussed. The input can be changed to any form by encoder and then change it back by decoder through inverse function. The concrete network can be seen as the memory stored by the parameters. Lethe is that when new knowledge input, the training process makes the parameters change. At last, the application of the network is discussed. The network can be used for logic generation through deep reinforcement learning. The network can also be used for language translation, zip and unzip, encryption and decryption, compile and decompile, modulation and demodulation.

The ongoing decommissioning of conventional power plants decreases the installed reactive power reserves for voltage control in transmission grids. Hence, an efficient planning of compensation devices substituting this lack of reactive power is required. Grid operators must allocate these devices for steady-state voltage control and for dynamic voltage control ensuring voltage stability. A separate determination of this static and dynamic VAR demand, however, fails to exploit synergies and disregards that VAR compensation in steady-state reduces the reserves for dynamic compensation. This paper proposes a coupled determination of the system static and dynamic VAR demand. An optimisation method applying mixed-integer programming identifies an efficient allocation and portfolio consisting of different compensation technologies. It includes constraints for voltage limits during steady-state and contingencies as well as for long-term voltage stability. Results emphasise that the method identifies an efficient portfolio for various operation and fault scenarios, while providing the required voltage stability margin.